Entity transaction attribute determination method and apparatus

ABSTRACT

An entity transaction attribute determination method for determining an attribute state or an attribute category of a to-be-predicted entity on a preset transaction is provided. The method comprises obtaining a plurality of historical relational networks sequentially arranged under a temporal order; determining, for each of the historical relational networks and through vector fusion of neighbor nodes, a plurality of description vectors of the to-be-predicted entity; processing, through a pre-trained time-series neural network, the description vectors to obtain an output result; and determining, according to the output result, the attribute state or the attribute category of the preset transaction attribute for the to-be-predicted entity. The method improves the accuracy of predicting a preset transaction attribute of an entity through the analysis of the description vectors.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to and benefits of the Chinese PatentApplication No. 202010724053.3, filed on Jul. 24, 2020, which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

This specification is related to the field of computer technologies, andin particular, to an entity transaction attribute determination methodand apparatus through computers.

BACKGROUND

Along with the development of computer technologies, assistance fromcomputers is becoming increasingly indispensable for daily transactionprocessing. For example, on a shopping platform, computers may recordthe products browsed, clicked, and purchased by users to recommendproducts that may be more appealing to the users. In news APPs onterminals, the terminals may record the pages searched for and browsedby users, thereby recommend more suitable news articles for users.Various similar examples exist but are not elaborated further herein.Particularly, developing artificial intelligence technology has enabledcomputers to handle daily transactions more intelligently. For example,a shopping platform may predict a certain transaction attribute (such asgender) of a user to pitch to the user products that are suitable forthe corresponding transaction attribute.

In exemplary applications, some transaction attributes of an entitybeing processed may be fixed, such as a gender transaction attribute ofthe user, while other transaction attributes may change under unexpectedevents. For example, an income transaction attribute of a user maychange under events such as a job change, losing jobs, health status,etc. A finance-related overdue risk transaction attribute of a user willbe subjected to a greater change upon events such as a change inoccupation, a change of the family members of the user, and a switch ofa loan platform of the user from a bank to a small loan company. Varioussimilar examples exist but are not elaborated further herein.

Therefore, a solution is needed to integrate the influence of suchunexpected events into an artificial intelligence-based prediction ofentity transaction attributes to improve the prediction accuracy.

SUMMARY

One or more embodiments of this specification describe an entitytransaction attribute determination method and apparatus, solving one ormore technical problems mentioned in the Background section.

The first aspect of this specification is directed to an entitytransaction attribute determination method for determining an attributestate or an attribute category of a to-be-predicted entity on a presettransaction attribute.

The method may include: obtaining a plurality of historical relationalnetworks sequentially arranged under a temporal order. The historicalrelational networks may respectively correspond to historical timepoints having a preset time interval. Each of the historical relationalnetworks may include a plurality of nodes, and each node may correspondto a plurality of entities comprising the to-be-predicted entity.

Each of the historical relational networks may be configured to describeassociation relationships between a plurality of entities thatcorrespond to a corresponding historical time point, and the entitieshaving an association relationship may be connected by a connecting edgetherebetween. Each node may correspond to a node vector composed oftransaction characteristics extracted based on descriptive informationof the corresponding entities.

The method may further include determining, for each of the historicalrelational networks and through vector fusion of neighbor nodes, aplurality of description vectors of the to-be-predicted entity;processing, through a pre-trained time-series neural network, thedescription vectors to obtain an output result; and determining,according to the output result, the attribute state or the attributecategory of the to-be-predicted entity on the preset transactionattribute.

In some embodiments, the time-series neural network may be trained by:obtaining a current training sample corresponding to a sample entity andan attribute tag of the sample entity regarding the preset transactionattribute; executing, for each historical relational network of Thistorical relational networks sequentially arranged under the temporalorder, a node vector fusion operation of the neighbor nodes, todetermine T description vectors respectively corresponding to the sampleentity under the T relational networks, wherein T is an integer, and theT historical relational networks may be established under T time pointshaving the preset time interval; sequentially inputting, under thetemporal order of the corresponding historical relational networks, theT description vectors into a selected time-series neural network, andobtaining a sample output result of the time-series neural network forthe sample entity; and adjusting, with the objective of matching thesample output result with the attribute tag, model parameters to trainthe time-series neural network.

In some embodiments, the transaction characteristics in the node vectorcorresponding to a single node may be extracted through descriptiveinformation at a corresponding historical time point of a correspondingrelational network, or extracted through descriptive information withina time interval between the corresponding historical time point and aprevious historical time point of the corresponding relational network.

In some embodiments, the to-be-predicted entity may be a user. Theentities in each of the historical relational networks may comprise aplurality of users and at least one of: an IP identifier or a WIFIidentifier for a user device to access a network, and an applicationinstalled on the user device.

In some embodiments, the historical relational networks may include afirst relational network. The to-be-predicted entity may correspond to afirst node in the first relational network. The determining, for each ofthe historical relational networks and through vector fusion of theneighbor nodes, a plurality of description vectors of theto-be-predicted entity may include: processing the relational networkthrough a multilayer graph neural network, the multilayer graph neuralnetwork updating, after processing of a current layer is done, the nodevector of the first node based on a weighted result from a product ofthe node vector of each neighbor node updated by the previous layer andan auxiliary weight matrix; and using the node vector of the first nodeupdated by the last layer of the graph neural network as a firstdescription vector of the first node corresponding to the firstrelational network.

In some embodiments, the neighbor nodes of the first node may comprise asecond node. At the current layer, a weight of the second node relativeto the first node may be determined by: an exponential form-basednormalization result of a similarity level between the node vector ofthe first node updated by the previous layer and the node vector of thesecond node updated by the previous layer, relative to a sum ofsimilarity levels between the node vector of the first node updated bythe previous layer and the node vector of each of the neighbor nodesupdated by the previous layer.

In some embodiments, the similarity level between the node vector of thefirst node updated by the previous layer and the node vector of thesecond node updated by the previous layer is determined by:concatenating the node vector of the first node updated by the previouslayer and the node vector of the second node updated by the previouslayer to form a concatenation vector; performing, through a spatialauxiliary weight matrix, a dimensionality reduction process on theconcatenation vector to obtain an intermediate vector having presetdimensions; and processing, through an auxiliary vector having thepreset dimensions, the intermediate vector to obtain the similaritylevel between the node vector of the first node updated by the previouslayer and the node vector of the second node updated by the previouslayer.

Ins some embodiments, the output result may be an output vector. Thedetermining, according to the output result, the attribute state or theattribute category of the to-be-predicted entity on the presettransaction attribute may comprises: processing the output vectorthrough a fully connected neural network; and determining, through anobtained processing result, the attribute state or the attributecategory of the to-be-predicted entity on the preset transactionattribute.

In some embodiments, the output result may be an output vector, theattribute states or attribute categories of the to-be-predicted entityon the preset transaction attribute may further respectively correspondto description vectors. The determining, according to the output result,the attribute state or the attribute category of the to-be-predictedentity on the preset transaction attribute may comprise: determiningrespective corresponding similarity levels between the output vector andeach of the description vectors; and determining an attribute state orattribute category corresponding to a description vector having thehighest similarity level as the attribute state or attribute category ofthe to-be-predicted entity on the preset transaction attribute during apreset time period.

Another aspect of this specification is directed to an entitytransaction attribute determination apparatus for determining, through aplurality of historical relational networks based on a temporal order,an attribute state or an attribute category of a to-be-predicted entityon a preset transaction attribute. The apparatus may include: adetermining unit, configured to obtain a plurality of historicalrelational networks sequentially arranged under a temporal order,wherein the historical relational networks respectively correspond tohistorical time points having a preset time interval; each of therelational networks comprises a plurality of nodes, each nodecorresponds to a plurality of entities comprising the to-be-predictedentity, each of the historical relational networks is configured todescribe association relationships between a plurality of entities thatcorrespond to a corresponding historical time point, the entities havingan association relationship are connected by a connecting edgetherebetween, and each node corresponds to a node vector composed oftransaction characteristics extracted based on descriptive informationof the corresponding entities; a fusion unit, configured to determine,for each of the relational networks and through vector fusion of theneighbor nodes, a plurality of description vectors of theto-be-predicted entity; a time-series data processing unit, configuredto process, through a pre-trained time-series neural network, thedescription vectors to obtain an output result; and a prediction unit,configured to determine, according to the output result, the attributestate or the attribute category of the to-be-predicted entity on thepreset transaction attribute.

Another aspect of this specification is directed to a computer-readablestorage medium. The storage medium may have a computer program storedthereon. When the computer program is executed in a computer, thecomputer may be caused to execute the method of the first aspect.

Another aspect of this specification is directed to a computing device.The computing device may comprise a memory and a processor. Executablecode may be stored in the memory. When executing the executable code,the processor may implement the method of the first aspect.

Another aspect of this specification is directed to an entitytransaction attribute determination apparatus. The apparatus may includea processor and a non-transitory computer-readable memory coupled to theprocessor and configured with instructions executable by the processorto perform any of the entity transaction attribute determination methodsdescribed above.

Another aspect of this specification is directed to a non-transitorycomputer-readable storage medium. The storage medium may haveinstructions stored thereon executable by a processor to cause theprocessor to perform any of the entity transaction attributedetermination methods described above.

Through the method and apparatus provided in the embodiments of thisspecification, on the one hand, description vectors of a to-be-predictedentity at different historical time points may be processed through atime-series neural network to consider time-series characteristics ofthe to-be-predicted entity. On the other hand, node vector aggregationof the neighbor nodes may be performed for the description vector of asingle historical time point based on a relational network determinedunder entity states of a corresponding historical time point, fullyexploring the influence of a neighbor entity on the current entitystate, thereby generating a description vector having a betterdescriptive capability for users. Further, the accuracy of predictingentity transaction attributes is improved by processing the historicalrelational network having time-series characteristics.

BRIEF DESCRIPTION OF THE DRAWINGS

To describe the technical solutions in the embodiments of thisspecification more clearly, the following briefly describes theaccompanying drawings required for describing the embodiments.Apparently, the accompanying drawings in the following description aresome embodiments of this specification, and a person of ordinary skillin the art may further derive other accompanying drawings from theseaccompanying drawings without inventive efforts.

FIG. 1 illustrates a schematic diagram of an implementation architectureof this specification.

FIG. 2 illustrates a schematic diagram of a relational network accordingto some embodiments.

FIG. 3 illustrates a flowchart of a method for training a time-seriesneural network according to some embodiments.

FIG. 4 illustrates a flowchart of an entity transaction attributedetermination method according to some embodiments.

FIG. 5 illustrates a schematic block diagram of an entity transactionattribute determination apparatus according to some embodiments.

DETAIL DESCRIPTION OF THE EMBODIMENTS

Embodiments of this specification are described below with reference tothe drawings.

FIG. 1 illustrates a schematic diagram of an implementation architectureof this specification. As shown in FIG. 1, under the implementationarchitecture of this specification, a relational network may bepre-established with multiple time points as reference points. Nodes inthe relational network may be various entities associated with an entityhaving a to-be-predicted transaction attribute.

For example, if the entity having a to-be-predicted transactionattribute is a user, the entities in the relational network may bemultiple users and entities related to user devices. A user herein mayrefer to a virtual user (different from a biological person), forexample, a terminal user, or a registered user of a network platform(website or APP). Generally, one user ID may correspond to one user.Such a user may typically interact with a server through a user device,so the user often relies on the user device to interact with the server.The entity related to the user device may reflect the characteristics ofthe user. That is, the entity is also associated with the user.

Similarly, the entity having a to-be-predicted transaction attribute maybe another type of entity (e.g., a product, a customer service questionand answer text, etc.). The characteristics of the entity having ato-be-predicted transaction attribute may also be described by otherrelated entities (such as a customer, a merchant, a customer raising acustomer service question, etc.) according to the exemplary applicationscenario. Therefore, under the technical concept of this specification,when a relational network is to be established, nodes in one-to-onecorrespondence with all entities comprising the entity having ato-be-predicted transaction attribute are provided, and nodes inone-to-one correspondence with other associated entities may be furtherprovided.

The association relationships between the entities corresponding to thenodes may be represented by connecting lines between the nodes. Wherethe entity having a to-be-predicted transaction attribute is a user,other associated entities may include, but are not limited to, anapplication installed on a user device, an IP identifier, WiFiidentifier, or communication base station identifier for a user deviceto access the Internet, an activity-related organization (e.g., afinancial activity-related organization is a bank or a smallhigh-interest loan company, etc.), and so on.

For each relational network, transaction characteristics of each entitymay be extracted according to relevant descriptive information of theentity to describe the corresponding entity. In some embodiments,corresponding transaction characteristics of users may be extractedaccording to their gender, income, employer, consumer preferences,capital flow, and other descriptive information to form a node vector.

Corresponding characteristics for the IP identifier, WiFi identifier, orcommunication base station identifier for a user device to access theInternet may be extracted according to the geographic locationinformation, the number of access users, and other descriptiveinformation of the identifier to form a node vector. Correspondingtransaction characteristics of an application installed on a user devicemay be extracted according to the download volume, release time, andapplication category (e.g., shopping, gaming, etc.), and otherdescriptive information to form a node vector. Corresponding transactioncharacteristics of an activity-related organization may be extractedaccording to the establishment time, user volume, asset category, scaledtotal asset number, organization category, and other descriptiveinformation to form a node vector. Various similar examples exist butare not elaborated further herein.

In some embodiments, the vectors of various nodes may have uniformdimensions. For example, nodes corresponding to entities in differentcategories may respectively correspond to characteristics having thecorresponding dimensions. The number of dimensions of each node vectormay be a sum of the dimensions of each of the entity categories, and thenode vector may include values in the corresponding dimensions. In oneexample, the number of entity categories may be 3, including the user,the APP, and the activity-related organization. If the transactioncharacteristics of the user is 5-dimensional, the transaction feature ofthe APP is 4-dimensional, and the transaction characteristics of theactivity-related organizations is 3-dimensional, then the total numberof dimensions for the transaction characteristics of the entitycategories is 12. Here, each node may be represented by a 12-dimensionalnode vector. A single node may have meaningful characteristic valuesonly in the corresponding dimensions. For example, a user may havecharacteristic values in the first 5 dimensions of the 12-dimensionalnode vector, and the last 7 dimensions all have a preset value (e.g.,0).

A plurality of description vectors of an entity requiring transactionattribute prediction may be determined by fusing characteristics ofneighbor nodes in each relational network. Then, as shown in FIG. 1,description vectors arranged in a temporal order may be sequentiallyinputted into the time-series neural network to obtain an output result.The output result may correspond to a preset transaction attribute of auser.

The technical concept of this specification is described in detailbelow.

Under the technical concept of this specification, the involvedtechnical problems may include the construction of a relational network,the vector fusion of neighbor nodes in a relational network, and theprocessing of each description vector by the time-series neural network.The following describes these technical problems and each of theimplementation solutions to these problems.

First, the relational network involved in this specification may beconstructed by collecting information of corresponding entities undertime points having a preset time interval. The time points may beregarded as the time basis for data truncation. The preset time intervalmay be, for example, one month or one week, which is not limited in thisspecification.

The exemplary collected entity information may be various pieces ofinformation at the current time point corresponding to the relationalnetwork or entity information during the time interval between thecurrent time point and the previous time point. In some embodiments, fora physical APP related to a user device, a user download characteristicmay be the total user downloads up to the corresponding time point, orthe user downloads during the time interval between the correspondingtime point and the previous time point. For a user, an occupationcharacteristic may be the occupation of the user at the correspondingtime point, or various occupations which the user has taken up during apreset time interval before the corresponding time point. Varioussimilar examples exist but are not elaborated further herein.

A corresponding relational network may be constructed through variousentities related to the entity having a to-be-predicted transactionattribute. The nodes in the relational network may correspond to theentities in a one-to-one manner. A user may have an associationrelationship with entities such as other users, the APPs installed onthe user device into which the user is logged, the WIFI address or IPaddress for accessing the Internet, and the like. The associationrelationship between entities may be represented by a connecting edge ormay be represented by a preset identifier in a three-tuple. An n-tuple(n is a non-negative integer) may refer to a sequence (or ordered list)of n elements. Thus, a three-tuple may refer to a sequence (or orderedlist) of three elements.

In some embodiments, the association relationship between a user entityand another user entity may be one determined by activities such asbeing a contact in the address book, performing a wire transfer, sendinga red envelope, etc. When such an association relationship existsbetween two users, the two corresponding entities in the relationalnetwork may be connected by a connecting edge.

In FIG. 2, an entity with a to-be-predicted transaction attribute beinga user is taken as an example, and a schematic diagram of a relationalnetwork corresponding to a time point is shown. In FIG. 2, the involvedentities may include, but are not limited to: users, APPs associatedwith a user device, WIFI networks for a user device to access theInternet.

As shown in FIG. 2, user A has association relationships with user C,APP1, and WIFI1. User B has association relationships with APP1, APP2,and APP3. User C has association relationships with APP2, user E, userA, and WIFI2. User E has association relationships with user C, APP3,and WIFI2. User D has association relationships with APP1, APP2, WIFI1,and WIFI2. The relational network in FIG. 2 may describe the associationrelationships between the users and other entities. As an example, otherassociated entities in FIG. 2 include APPs and WIFI networks. Otherassociated entities may be various entities related to theto-be-predicted entity.

Each node may have corresponding descriptive information. Thecorresponding transaction characteristics may be extracted from thedescriptive information to form a characteristic vector, which may beused as an initial node vector describing the corresponding entity.

For each of the time points with a preset interval, a correspondingrelational network may be constructed to describe the associationrelationships between various entities. Each of the relational networksmay have node vectors determined according to the current descriptiveinformation of the corresponding entities. States of the entities ateach of the time points arranged under a time interval may becharacterized, thereby describing, in a temporal order, the states ofthe entity having a to-be-predicted transaction attribute at differenttimes.

Second, vector fusion may be performed for vectors of neighbor nodes inan individual relational network. A node may have various associationswith neighbor nodes, and such associations may reflect the implicitproperties of the entity. In some embodiments, a user having anassociation relationship with a game APP may demonstrate the user'scurrent interest in the game APP. Users interested in the game APP mayshare similar traits, such as being a student, being unemployed, havinga stress-free occupation. The vector fusion of neighbor nodes fuses theinformation of the neighbor nodes for each node, thereby enhancing thedescriptive capability of a node vector for a node.

In some embodiments, the node vectors of neighbor nodes within a presetorder with respect to the current node may be fused together throughmethods such as a weighted average, a maximum value, etc., to obtain theneighbor fusion result, which may be further fused with the node vectorof the current node. The obtained fusion vector may be the descriptionvector of the current node. In practice, other methods may be used forthe neighbor node fusion.

In some embodiments, the current node may be considered as its ownneighbor node to perform the neighbor-node vector fusion.

In some embodiments, some nodes may be selected from neighbor nodes toperform the neighbor-node vector fusion.

In some embodiments, node vectors of neighbor nodes within a presetorder with respect to the current node may be fused layer-by-layer in aniterative manner from the outermost layer to the innermost layer.

In some embodiments, the preset order may be 3, which means that theneighbor-node vector fusion may first be performed on the third-orderneighbor nodes of the current node, and the node vectors of thethird-order neighbor nodes after the fusion may perform neighbor-nodevector fusion on the second-order neighbor nodes. Then, neighbor-nodevector fusion may be performed on the first-order neighbor nodes.Finally, the node vectors of the first-order neighbor nodes with theneighbor-node vector fusion performed is fused with the current node.

In some embodiments, all nodes in the relational network may betraversed to perform node vector fusion of neighbor nodes on each nod,causing the current node vector of each node to be updated. Aftermultiple iterations, the current node vector of the current node may bethe description vector of the current node in the individual relationalnetwork.

Various similar examples exist but are not elaborated further herein.

In some embodiments, an individual relational network may be processedthrough a graph neural network (GNN), causing the node vector fusion ofneighbor nodes to be performed on each node. For the graph neuralnetwork, each neural network layer means traversing each node in therelational network and updating the current node vector of each node asthe fusion vector for the vectors of the neighbor nodes of the currentnode.

As an example, when the relational network is processed through a graphneural network, the (l+1)-th layer of the neural network may update thenode vector of node u to be a function of a weighted result based onsetting the weight of each neighbor node as an attention value:h _(u) ^(l+1) =f(Σ_(v∈N) _(u) α(u,v)h _(v) ^(l) W ^(l))where h_(u) ^(l+1) represents the node vector of the node u after beingupdated by the (l+1)-th layer, α(u, v) represents the importancecoefficient of the node v for the node u in the (l+1)-th layer of thegraph neural network, h_(v) ^(l) represents the node vector of the nodeu after being updated by the l-th layer, N_(u) represents a set of allor some (e.g., no more than 5) of the neighbor nodes of node u within apreset order (e.g., 1). Generally, the neighbor nodes may include node uitself, and W^(l) represents the auxiliary weight matrix in the (l+1)-thlayer of the graph neural network and may change the dimensions of thenode vector. W^(l) is a model parameter that may be determined bytraining the training samples.

In an individual relational network, considering that different neighbornodes have different degrees of influence on the current node, theimportance coefficient is used to distinguish the importance of eachneighbor node for node u. In some embodiments, the node u may have thegreatest influence on itself and may have a large importancecoefficient. The importance coefficient of each neighbor node may be,for example, preset, or may be the similarity level between thedescription vector of the neighbor node and that of the current node. Inan example, the importance coefficient of each neighbor node may be anattention value determined according to the similarity level betweeneach of the neighbor node and the current node.

In some embodiments, the similarity between node v and node u may berepresented by a similarity level. The importance coefficient of node vrelative to node u (α(u, v)) may be an exponential form-basednormalization result of the similarity level between the node vector ofnode v and the node vector of node u relative to a sum of the similaritylevels between the node vector of each neighbor node of node u and thenode vector of node u. For example, the importance coefficient (α(u, v))may be:

${a\left( {u,\nu} \right)} = \frac{\exp\left( {s\left( {u,\nu} \right)} \right)}{\sum\limits_{j \in N_{u}}^{\;}\;{\exp\left( {s\left( {u,j} \right)} \right)}}$where s(u, v) represents the similarity level value between node u andnode v. In one example, the similarity level value may be represented bya dot product of the respective node vectors corresponding to node u andnode v. In another example, the similarity level value may be determinedby:s(u,v)=a ^(T) ·W _(spatial)·(h _(u) ∥h _(v))where “∥” represents the concatenation of two vectors. W_(spatial)represents an auxiliary weight matrix for the vector space and is usedto reduce the dimensions of a vector or for mapping to the correspondingcharacteristic space having preset dimensions. A similarity level valuemay be obtained through processing the auxiliary vector a^(T) byreducing the vector dimensions or mapping to the vector of thecorresponding characteristic space, and W_(spatial) and a^(T) are modelparameters that may be determined by training the training samples.

The transaction characteristics described by the node vectors in themulti-order neighborhood may be fused into the current node (node u)through the multilayer graph neural network, thereby obtaining adescription vector having a better descriptive capability.

Further, for the relational network corresponding to each time point,one description vector of the entity having a to-be-predictedtransaction attribute may be obtained. Since the time points arearranged a temporal order, respective description vectors may also havea corresponding temporal order.

Finally, each description vector may be processed by the time-seriesneural network. The time-series neural network may be a neural networkfor processing various data having a temporal order, such as a recurrentneural network (RNN). An RNN is a time-recursive neural network that mayprocess sequential data. In RNNs, a current output of a sequence isrelated to the previous output. For example, an RNN memorizes andapplies the previous information in the calculation of the currentoutput (i.e., the nodes of the hidden layers are connected). The inputfor the hidden layers includes not only the output of the input layerbut also the output of the hidden layers at the previous time instance.In the time-series diagram of the recurrent neural network in FIG. 3,the hidden layer state at the t-th iteration may be expressed as:S _(t) =f(U*X _(t) +W*S _(t−1)),where X_(t) is the state of the input layer at the t-th iteration,S_(t−1) is the state of the hidden layer at the (t−1)-th iteration, f isthe calculation function, and W and U are the weights. The RNN loops theprevious state back to the current input to take into account theinfluence of the historical input and therefore is suitable for atime-series data sequence.

In some embodiments, as shown in FIG. 1, the time-series neural networkmay be, for example, a Long Short-Term Memory (LSTM). For a neuron in anLSTM, at time instances of t−1, t, and t+1, the description vectors,arranged under an order, of the entity having a to-be-predictedtransaction attribute in the corresponding relational network of thecorresponding time period may be separately inputted. The descriptionvectors may be respectively represented by X_(t−1), X_(t), and X_(t+1).The states of the neuron at time instances of t−1, t, and t+1 may berespectively represented by S_(t−1), S_(t), and S_(t+1). The outputs attime instances of t−1, t, and t+1 may be respectively represented byC_(t−1), C_(t), C_(t−1), wherein:S _(t) =g(U*X _(t) +W*C _(t−1) +b _(s))C _(t) =f(V*S _(t−1) +b _(c))S _(t+1) =g(U*X _(t) +W*C _(t) +b _(s))C _(t+1) =f(V*S _(t) +b _(c))where U, W, and V are weights.

In the LSTM model, the current state of each neuron may be jointlydetermined by the input at the current time instance and the output atthe previous time instance. The current output of each neuron may berelated to the state at the previous time instance. The descriptionvectors of the entity at various time points may be analyzed using theLSTM model to selectively memorize information and perform data miningfor long-range dependence.

The main technical problems and implementation solutions involved in thetechnical concept of this specification are described in detail above.The complete technical solutions will be described below with someembodiments.

FIG. 3 illustrates the process of training a time-series neural networkaccording to some embodiments of this specification. The entityexecuting this process may be a computer, device, or server havingcertain computing capabilities. The model parameters in the time-seriesneural network may be adjusted by using multiple training samples totrain the time-series neural network.

As shown in FIG. 3, the process of training the time-series neuralnetwork comprises the following steps 301 through 304.

In step 301, a current training sample may be obtained. The currenttraining sample may correspond to a sample entity and an attribute tagof the sample entity with respect to the preset transaction attribute.

In step 302, for each historical relational network of T historicalrelational networks sequentially arranged under the temporal order, anode vector fusion operation of a neighbor node may be executed todetermine T description vectors corresponding to the sample entity underthe T historical relational networks. The T historical relationalnetworks being established under T time points may have the preset timeinterval.

In step 303, under the temporal order of the corresponding historicalrelational networks, the T description vectors may be sequentiallyinputted into a selected time-series neural network, and an outputsample result of the time-series neural network may be obtained for acurrent sample entity.

In step 304, with the objective of matching the output sample resultwith the attribute tag, model parameters may be adjusted to train thetime-series neural network.

In step 301, the current training sample, corresponding to the sampleentity and the attribute tag of the sample entity with respect to thepreset transaction attribute, may be obtained. In a supervised machinelearning model training process, training samples may be divided intomultiple batches, and one model parameter adjustment operation may beperformed for each batch. One batch of training samples may be onetraining sample or multiple training samples, which is not limited bythe embodiments herein. The current training sample may be the trainingsample of the current batch. The term “current” in the current batch maycorrespond to the current process. The current batch may be any batch.

One training sample may correspond to one piece of entity data. In thecase that the sample entity is a sample user, one piece of entity datamay indicate one sample user and a historical attribute tag of thesample user on the preset transaction attribute. The term “historicalattribute tag” is used herein because, for the training sample, thecorresponding attribute state or attribute category of the sample entityon the preset transaction attribute is already available.

Then, in step 302, for each historical relational network in the Thistorical relational networks arranged under temporal order, the nodevector fusion operation of a neighbor node may be performed to determinethe corresponding T description vectors of the sample entity under the Thistorical relational networks. T is an integer greater than 1.

Each historical relational network may describe the instantaneous stateof each entity at the corresponding historical time point. In order topredict, from entity states corresponding to the T historical timepoints, the attribute state (e.g., a scaled income number) or attributecategory (e.g., the gender attribute category being male) of the sampleentity on the preset transaction attribute, mining previous stateinformation before the corresponding attribute state or attributecategory on the preset transaction attribute is acquired by the user isrequired. Therefore, the T historical relational networks of the sampleentity are established at the T historical time points prior to thegeneration of the attribute tag. Since the relational network closer tothe time that the sample entity generates the attribute tag can reflectthe potential attribute state or attribute category of the sample entitybetter, in some embodiments, the historical time point corresponding tothe T-th historical relational network thus does not differ from thegeneration time of the attribute tag of the sample entity by a presettime threshold.

In one embodiment, the sample entity and the attribute tag of the sampleentity on the preset transaction attribute may be determined first.Then, the T historical relational networks may be established per apreset time interval by counting backward. For example, the timeinstance corresponding to the attribute tag of the sample entity may beJun. 1, 2020, the preset time threshold may be 1 week, and the presettime interval may be 1 month. Then, a time point within the period ofone week before Jun. 1, 2020 may be selected as the T-th historical timepoint, e.g., May 31, 2020. The (T−1)-th historical time points may beselected by counting backward using the preset time interval, e.g.,selecting T−1 time points including Apr. 30, 2020, Mar. 31, 2020, etc.Corresponding relational networks may be respectively established forthe T time points, including the T-th time point and the T−1 timepoints. The manner of establishing a relational network has beendescribed above and will not be further elaborated herein.

In some embodiments, the relational network at each time point may alsobe pre-established under a time interval. For example, the 15th of everymonth may be taken as a time point for establishing correspondingrelational networks under a time interval of 1 month. After the timeinstance corresponding to the attribute tag of the sample user isdetermined, T corresponding historical relational networks may beselected under the order of the corresponding historical time pointsfrom the closest to the most distant in time.

Further, for each of the T historical relational networks, onedescription vector may be determined under the manner described above,which yields T description vectors. Since the T historical relationalnetworks are arranged under the temporal order, these T descriptionvectors may also have time-series characteristics. If there are multiplecurrent sample entities, the attribute tags corresponding to thesemultiple sample entities may have different time instances, so each ofthe sample entities may correspond to its own attribute tag timeinstance. Similarly, each sample entity may correspond to its own Thistorical relational networks and T description vectors.

One graph neural network may process the T historical relationalnetworks. Alternatively, T graph neural networks may respectivelyprocess the T historical relational networks. This specification is notlimited in this regard. Processing a relational network with a graphneural network has been described above and will not be furtherelaborated herein.

Then, in step 303, under the temporal order of the correspondinghistorical relational networks, the T description vectors may besequentially inputted into a selected time-series neural network toobtain a sample output result of the time-series neural network for thesample entity.

The time-series neural network can process data having time-seriescharacteristics. For one exemplary training sample, the to-be-processeddata may be T description vectors arranged under the time series. Thetime-series neural network may include one or more neurons, which mayrespectively receive the T description vectors at T different timeinstances. One output result may be obtained through the processingmanner described above. The output result referred may correspond to theabove-described sample entity, and therefore may be called the sampleoutput result. The sample output result may be an output result obtainedafter the above-described one or more neurons having processed the T-thdescription vector or may be a processing result obtained by combining Toutput results, which is not limited by the embodiments herein.

The output result may be a vector or a score (a scaler). The sampleoutput result herein may indicate a prediction result of the time-seriesneural network for the attribute state or attribute category of thesample entity on the preset transaction attribute. In the case that abatch of training samples includes multiple sample entities, aprediction result of the attribute state or attribute category on thepreset transaction attribute may be obtained for each of the sampleentities.

Then, in step 304, with the objective of matching the output sampleresult with the attribute tag, model parameters may be adjusted to trainthe time-series neural network, according to the form of the outputsample result and the attribute tag. The consistency between these twomay be described by a vector or a numerical value.

In some embodiments, in the case that the sample output result is avector, the similarity level between the vector corresponding to thecorresponding attribute tag and the vector of the sample output result(e.g., a similarity level expressed by the dot product of the vectors)may describe the consistency between the sample output result and thesample attribute tag.

In some embodiments, in the case that the sample output result is ascore, the similarity level between these two may be described as theabsolute difference between the score corresponding to the sampleattribute tag and the score of the sample output result. Generally, asmaller absolute difference indicates a greater similarity level betweenthe sample attribute tag and the sample output result.

If the sample output result contradicts the attribute tag, then thedifference between them may be defined as a loss, which may be describedby a model parameter. The time-series neural network may be trained byadjusting the model parameters to reduce the loss.

In some embodiments, the sample output result may be a score. Sequentialadjustment operations of the model parameters may be performed based ona batch of training samples, including multiple sample entities, and theloss may be determined in the form of cross-entropy.

In some embodiments, take the binary classification result (which maycorrespond to a standard value of 0 or 1) as an example, the loss L maybe determined by:

$\mathcal{L} = {{{- \frac{1}{V}}{\sum\limits_{u \in V}{y_{u}\log\;\left( h_{u} \right)}}} + {\left( {1 - y_{u}} \right)\log\;\left( {1 - h_{u}} \right)}}$where V represents the sample entity sets respectively corresponding tothe training samples of the current batch. h_(u) represents the outputscore corresponding to node u (or sample entity u), and y_(u) representsthe attribute tag corresponding to node u (such as sample user u). Inthe case that the binary classification result is represented by 0 or 1,one of y_(u) or 1−y_(u) is 0. Using y_(u) being 0 as an example, theny_(u) log(h_(u)) is 0, and 1−y_(u) is 1. Therefore, the closer h_(u) to1, the closer log(1−h_(u)) to 0, and the smaller the loss. The closerh_(u) to 0, the closer log(1−h_(u)) to negative infinity, and thegreater the loss. The loss is negatively correlated with h_(u). The samereasoning may be applied to the case of y_(u) being 1, which will not befurther elaborated herein.

In some embodiments, in step 302, the parameters involved in the nodevector fusion operation of the neighbor node executed for eachhistorical relational network in the T historical relational networksmay also need to be adjusted. Therefore, the node fusion operation maybe combined with the time-series neural network to form an integratedmachine learning model, and the model parameters may be adjustedaccording to the above-described loss.

FIG. 4 illustrates the process of determining an entity transactionattribute according to some embodiments of this specification. Thisprocess is used for determining, through a plurality of historicalrelational networks based on a temporal order, an attribute state or anattribute category of a to-be-predicted entity on a preset transactionattribute.

The entity executing this process may be a computer, device, or serverhaving certain computing capabilities. The preset transaction attributeof the to-be-predicted entity has a temporal characteristic (i.e.,time-sensitive). In other words, the attribute state or attributecategory of the preset transaction attribute is valid for a certain timepoint or time period.

In some embodiments, an income transaction attribute of a user in aperformance-based salary system may only be valid for a certain month. Aloan overdue risk transaction attribute of a user in the creditfinancing field may also be only valid for the current month or even bevalid only up to the payment due date.

As shown in FIG. 4, the process of determining a transaction attributeof a to-be-predicted entity may comprise the following steps 401 through404.

In step 401, a plurality of historical relational networks sequentiallyarranged under a temporal order may be obtained. The historicalrelational networks may respectively correspond to historical timepoints having a preset time interval. Each of the historical relationalnetworks may comprise a plurality of nodes, and each node may correspondto a plurality of entities comprising the to-be-predicted entity. Eachof the historical relational networks may be configured to describeassociation relationships between various entities that correspond to acorresponding historical time point. Entities having an associationrelationship may be connected by a connecting edge therebetween, andeach node corresponds to a node vector composed of transactioncharacteristics extracted based on descriptive information of thecorresponding entities.

In step 402, for each of the historical relational networks and throughvector fusion of the neighbor nodes, each description vector of theto-be-predicted entity may be determined.

In step 403, through a pre-trained time-series neural network, eachdescription vector may be processed to obtain an output result.

In step 404, according to the output result, the attribute state or theattribute category of the to-be-predicted entity on the presettransaction attribute may be determined.

In step 401, the multiple historical relational networks sequentiallyarranged under the temporal order may be obtained. Each of thehistorical relational networks may include multiple nodes. Each node maycorrespond to multiple entities. These entities may include theto-be-predicted entity. These entities may belong to the same categoryas the to-be-predicted entity or related categories.

In some embodiments, in the case that the to-be-predicted entity is auser, the multiple entities in an individual relational network mayinclude multiple users and multiple other entities associated with theuser, such as an APP installed on a user device, an IP address for auser device to access the network, etc. An individual historicalrelational network may be configured to describe the associationrelationships between various entities at the corresponding historicaltime point. The entities having an association relationship may beconnected by a connecting edge therebetween. Each node may correspond toa node vector composed of transaction characteristics extracted based onthe corresponding descriptive information. The construction process ofthe relational network has been described above, which will not befurther elaborated.

Because the preset transaction attribute of the to-be-predicted entityhas a temporal characteristic, the multiple historical relationalnetworks herein may be associated with the temporal characteristic ofthe to-be-predicted transaction attribute. The multiple historicalrelational networks herein may be relational networks respectivelycorresponding to multiple historical time points close to the currenttime. These historical time points may be selected under a preset timeinterval. In order to ensure validity, the time difference between thelast historical time point and the current time may not exceed a presettime threshold.

The multiple historical relational networks may be pre-established underthe preset time interval and may be selected according to the temporalcharacteristic when the process in FIG. 4 is executed. Alternatively,the multiple historical relational networks may be established byselecting multiple historical time points at the instant when theprocess in FIG. 4 is executed. The number of historical relationalnetworks herein may be preset, for example, as T or other values in theprocess in FIG. 3. Determining the multiple historical relationalnetworks is similar to that described in step 302 and will not befurther elaborated herein.

Then, in step 402, for each of the historical relational networks andthrough vector fusion of a neighbor node, each description vector of theto-be-predicted entity may be determined. The vector fusion of theneighbor nodes has been described above and will not be furtherelaborated herein.

Then, in step 403, through a pre-trained time-series neural network,each description vector may be processed to obtain an output result. Instep 403, a time-series neural network having the model parameter(s)adjusted by the process in FIG. 3 may sequentially process eachdescription vector under the temporal order of each historical timepoint corresponding to each historical relational network, therebyobtaining the output result of the time-series neural network. Theoutput result may be output after the neurons of the time-series neuralnetwork have processed the last description vector or an outputtedfusion result after each description vector has been processed, which isnot limited by the embodiments herein.

In addition, the output result of the time-series neural network may bea vector or a score. In one example, a binary classification task mayoutput a score to indicate the probability of being classified into acertain category. In another example, each category may be representedby a vector, and the similarity level between the output vector and eachcategory vector may describe the category likelihood. In yet anotherexample, the output result may be a vector that can be mapped to anexemplary classification category through the processing of a fullyconnected network processing. Various similar examples exist but are notelaborated herein.

Furthermore, in step 404, according to the output result, the attributestate or the attribute category of the to-be-predicted entity on thepreset transaction attribute may be determined. The output result of thetime-series neural network may be a vector or a score according to thedescription of step 403. Corresponding to the output result, the mannerof determining the preset transaction attribute of the to-be-predictedentity according to the output result may be as follows.

In one embodiment, the output result for a binary classification taskmay be a score indicating the probability of being classified into acertain category. Then, the attribute state or attribute category of theto-be-predicted entity on the preset transaction attribute during apreset time period may be determined according to a preset scorethreshold.

In another embodiment, each category may be represented by a vector. Thesimilarity level between the output vector and each category vector maydescribe the category likelihood. Then, an attribute state or attributecategory corresponding to the category vector having the highestsimilarity level with the output vector may be determined as theattribute state or attribute category of the to-be-predicted entity onthe preset transaction attribute.

In yet another embodiment, the output result may be a vector, which maybe processed by a fully connected network to map the vector to anexemplary classification category.

In some embodiments, other manners may be adopted to determine,according to the output result, the attribute state or attributecategory of the to-be-predicted entity on the preset transactionattribute during the preset time period. For example, the presettransaction attribute may be the scaled income number, the output resultmay be a score, and different scores may correspond to different scaledincome numbers (e.g., a score of 5 may correspond to an income of 5,000RMB, etc.).

To sum up the above process, on the one hand, the method provided by theembodiments of this specification may process description vectors of ato-be-predicted entity at different historical time points through atime-series neural network to take into account time-seriescharacteristics of the to-be-predicted entity. On the other hand, nodevector aggregation of neighbor nodes may be performed for thedescription vector of a single time point based on a relational networkdetermined under corresponding entity states at the correspondinghistorical time points, fully considering influences from other entitiesassociated with the to-be-predicted entity on the entity state, therebygenerating a description vector having a better descriptive capabilityfor the to-be-predicted entity. Such a description vector having thetime-series characteristic and the characteristics of being fused withneighbor nodes may fully exploit the influences of environmental changeson entity attributes, thereby improving the accuracy of predictingentity transaction attributes.

According to another aspect, an entity transaction attributedetermination apparatus is further provided. The apparatus may be usedfor determining, through a plurality of historical relational networksbased on a temporal order, an attribute state or an attribute categoryof a to-be-predicted entity on a preset transaction attribute.

As shown in FIG. 5, an entity transaction attribute determinationapparatus 500 may comprise a determining unit 51, a fusion unit 52, atime-series data processing unit 53, and a prediction unit 54.

The determining unit 51 may be configured to obtain a plurality ofhistorical relational networks sequentially arranged under a temporalorder, wherein the historical relational networks respectivelycorrespond to historical time points having a preset time interval. Eachof the relational networks may include a plurality of nodes, and eachnode may correspond to a plurality of entities comprising theto-be-predicted entity. Each of the historical relational networks maybe configured to describe association relationships between theplurality of entities that correspond to a corresponding historical timepoint. The entities having an association relationship may be connectedby a connecting edge therebetween, and each node may correspond to anode vector composed of transaction characteristics extracted based ondescriptive information of the corresponding entities.

The fusion unit 52 may be configured to determine, for each of therelational networks and through vector fusion of neighbor nodes, aplurality of description vectors of the to-be-predicted entity.

The time-series data processing unit 53 may be configured to process,through a pre-trained time-series neural network, the descriptionvectors to obtain an output result.

The prediction unit 54 may be configured to determine, according to theoutput result, the attribute state or the attribute category of theto-be-predicted entity on the preset transaction attribute.

In some embodiments, the transaction characteristics in the node vectorcorresponding to a single node are extracted through descriptiveinformation at a corresponding historical time point of a correspondingrelational network, or extracted through descriptive information withina time interval between the corresponding historical time point and aprevious historical time point of the corresponding relational network.

In some embodiments, in the case that the to-be-predicted entity is auser, the entities in each of the historical relational networks mayinclude a plurality of users and at least one of: an IP identifier or aWIFI identifier for a user device to access a network, and anapplication installed on the user device.

In some embodiments, the historical relational networks may include afirst relational network. The to-be-predicted entity may correspond to afirst node in the first relational network.

The fusion unit 52 may be further configured to process the relationalnetwork through a multilayer graph neural network. The multilayer graphneural network may update, after processing of a current layer is done,the node vector of the first node based on a weighted result from aproduct of the node vector of each neighbor node updated by the previouslayer and an auxiliary weight matrix. The fusion unit 52 may be furtherconfigured to use the node vector of the first node updated by the lastlayer of the graph neural network as a first description vector of thefirst node corresponding to the first relational network.

In some embodiments, the neighbor nodes of the first node may include asecond node, and at the current layer, the fusion unit 52 may be furtherconfigured to determine a weight of the second node relative to thefirst node by an exponential form-based normalization result of asimilarity level between the node vector of the first node updated bythe previous layer and the node vector of the second node updated by theprevious layer, relative to a sum of similarity levels between the nodevector of the first node updated by the previous layer and the nodevector of each of the neighbor nodes updated by the previous layer.

In some embodiments, the fusion unit 52 may be configured to determinethe similarity level between the node vector of the first node updatedby the previous layer and the node vector of the second node updated bythe previous layer by: concatenating the node vector of the first nodeupdated by the previous layer and the node vector of the second nodeupdated by the previous layer to form a concatenation vector;performing, through a spatial auxiliary weight matrix, a dimensionalityreduction process on the concatenation vector to obtain an intermediatevector having preset dimensions; and processing, through an auxiliaryvector having the preset dimensions, the intermediate vector to obtainthe similarity level between the node vector of the first node updatedby the previous layer and the node vector of the second node updated bythe previous layer.

In some embodiments, the prediction unit 54 may be further configuredto: process the output vector through a fully connected neural network;and determine, through the obtained processing result, the attributestate or the attribute category of the to-be-predicted entity on thepreset transaction attribute.

In some embodiments, the output result may be an output vector, andattribute states or attribute categories of the to-be-predicted entityon the preset transaction attribute may further respectively correspondto description vectors. The prediction unit 54 may be further configuredto: determine respective corresponding similarity levels between theoutput vector and each of the description vectors; and determine anattribute state or attribute category corresponding to a descriptionvector having the highest similarity level as the attribute state orattribute category of the to-be-predicted entity on the presettransaction attribute during a preset time period.

The apparatus 500 in FIG. 5 may be an apparatus embodiment correspondingto the method embodiment in FIG. 4, and the corresponding descriptionfor the method embodiment in FIG. 4 also applies to the apparatus 500,which is not further elaborated.

According to another aspect, a computer-readable storage medium isfurther provided and has a computer program stored thereon, and when thecomputer program is executed in a computer, the computer is caused toexecute the method described with FIG. 3 or FIG. 4.

According to yet another aspect, a computing device is further provided.The computing device may include a memory and a processor, whereinexecutable code is stored in the memory. When executing the executablecode, the processor may implement the method described with FIG. 3 orFIG. 4.

According to yet another aspect, an entity transaction attributedetermination apparatus is provided. The apparatus may include aprocessor and a non-transitory computer-readable memory coupled to theprocessor. The computer-readable memory may be configured withinstructions executable by the processor to perform any of the entitytransaction attribute determination methods described above.

According to yet another aspect, a non-transitory computer-readablestorage medium is provided. The storage medium may have instructionsstored thereon executable by a processor to cause the processor toperform any of the entity transaction attribute determination methodsdescribed above.

One skilled in the art should appreciate that in one or more of theabove-mentioned examples, the functions described in the embodiments ofthis specification may be implemented by hardware, software, firmware,or any combination thereof. When implemented by software, thesefunctions may be stored in a computer-readable medium or transmitted asone or more instructions or code on the computer-readable medium.

The exemplary implementations described above illustrate, in furtherdetail, the objective, technical solutions, and beneficial effects ofthe technical concept of this specification. It should be appreciatedthat the above are some implementations of the technical concept of thisspecification and is not used to limit the protection scope of thetechnical concept of this specification. Any modification, equivalentalternatives, improvement made based on the technical solutions of theembodiments of this specification shall fall within the protection scopeof the technical concept of this specification.

What is claimed is:
 1. A method for entity transaction attributedetermination, comprising: obtaining a plurality of historicalrelational networks sequentially arranged under a temporal order,wherein the historical relational networks respectively correspond tohistorical time points having a preset time interval, each of thehistorical relational networks comprises a plurality of nodes, each nodecorresponds to a plurality of entities comprising the to-be-predictedentity, each of the historical relational networks is configured todescribe association relationships between the plurality of entitiescorresponding to a corresponding historical time point, and each nodecorresponds to a node vector composed of transaction characteristicsextracted based on descriptive information of the correspondingentities; determining, for each of the historical relational networksand through vector fusion of neighbor nodes, a plurality of descriptionvectors of the to-be-predicted entity; processing, through a pre-trainedtime-series neural network, the description vectors to obtain an outputresult; and determining, according to the output result, an attributestate or an attribute category of a preset transaction attribute for ato-be-predicted entity.
 2. The method of claim 1, wherein thetime-series neural network is trained by: obtaining a current trainingsample corresponding to a sample entity and an attribute tag of thesample entity regarding the preset transaction attribute; executing, foreach historical relational network of T historical relational networkssequentially arranged under the temporal order, a node vector fusionoperation of the neighbor nodes, to determine T description vectorsrespectively corresponding to the sample entity under the T relationalnetworks, wherein T is an integer, and the T historical relationalnetworks are established under T time points having the preset timeinterval; sequentially inputting, under the temporal order of thecorresponding historical relational networks, the T description vectorsinto a selected time-series neural network, and obtaining a sampleoutput result of the time-series neural network for the sample entity;and adjusting, with an objective of matching the sample output resultwith the attribute tag, model parameters to train the time-series neuralnetwork.
 3. The method of claim 1, wherein the transactioncharacteristics in the node vector corresponding to a single node areextracted through descriptive information at a corresponding historicaltime point of a corresponding relational network, or extracted throughdescriptive information within a time interval between the correspondinghistorical time point and a previous historical time point of thecorresponding relational network.
 4. The method of claim 1, wherein theto-be-predicted entity is a user, the entities in each of the historicalrelational networks comprise a plurality of users and at least one of:an IP identifier or a WIFI identifier for a user device to access anetwork, and an application installed on the user device.
 5. The methodof claim 1, wherein the historical relational networks comprise a firstrelational network, the to-be-predicted entity corresponds to a firstnode in the first relational network, and the determining, for each ofthe historical relational networks and through vector fusion of theneighbor nodes, the plurality of description vectors of theto-be-predicted entity comprises: processing the relational networkthrough a multilayer graph neural network, the multilayer graph neuralnetwork updating, after processing of a current layer is done, the nodevector of the first node based on a weighted result from a product of anode vector of each neighbor node updated by a previous layer and anauxiliary weight matrix; and using the node vector of the first nodeupdated by the last layer of the graph neural network as a firstdescription vector of the first node corresponding to the firstrelational network.
 6. The method of claim 5, wherein the neighbor nodesof the first node comprise a second node, and at the current layer, aweight of the second node relative to the first node is determined by:an exponential form-based normalization result of a similarity levelbetween the node vector of the first node updated by the previous layerand the node vector of the second node updated by the previous layer,relative to a sum of similarity levels between the node vector of thefirst node updated by the previous layer and the node vector of each ofthe neighbor nodes updated by the previous layer.
 7. The method of claim6, wherein the similarity level between the node vector of the firstnode updated by the previous layer and the node vector of the secondnode updated by the previous layer is determined by: concatenating thenode vector of the first node updated by the previous layer and the nodevector of the second node updated by the previous layer to form aconcatenation vector; performing, through a spatial auxiliary weightmatrix, a dimensionality reduction process on the concatenation vectorto obtain an intermediate vector having preset dimensions; andprocessing, through an auxiliary vector having the preset dimensions,the intermediate vector to obtain the similarity level between the nodevector of the first node updated by the previous layer and the nodevector of the second node updated by the previous layer.
 8. The methodof claim 1, wherein the output result is an output vector, thedetermining, according to the output result, the attribute state or theattribute category of the preset transaction attribute for theto-be-predicted entity comprises: processing the output vector through afully connected neural network; and determining, through an obtainedprocessing result, the attribute state or the attribute category of thepreset transaction attribute for the to-be-predicted entity.
 9. Themethod of claim 1, wherein the output result is an output vector, theattribute states or attribute categories of the preset transactionattribute for the to-be-predicted entity further respectively correspondto description vectors, and the determining, according to the outputresult, the attribute state or the attribute category of the presettransaction attribute for the to-be-predicted entity comprises:determining respective corresponding similarity levels between theoutput vector and each of the description vectors; and determining anattribute state or attribute category corresponding to a descriptionvector having the highest similarity level as the attribute state orattribute category of the preset transaction attribute for theto-be-predicted entity during a preset time period.
 10. An entitytransaction attribute determination apparatus, comprising a processorand a non-transitory computer-readable memory coupled to the processor,and configured with instructions executable by the processor to performoperations, comprising: obtaining a plurality of historical relationalnetworks sequentially arranged under a temporal order, wherein thehistorical relational networks respectively correspond to historicaltime points having a preset time interval, each of the historicalrelational networks comprises a plurality of nodes, each nodecorresponds to a plurality of entities comprising a to-be-predictedentity, each of the historical relational networks is configured todescribe association relationships between the plurality of entitiescorresponding to a corresponding historical time point, and each nodecorresponds to a node vector composed of transaction characteristicsextracted based on descriptive information of the correspondingentities; determining, for each of the historical relational networksand through vector fusion of neighbor nodes, a plurality of descriptionvectors of the to-be-predicted entity; processing, through a pre-trainedtime-series neural network, the description vectors to obtain an outputresult; and determining, according to the output result, an attributestate or an attribute category of the preset transaction attribute forthe to-be-predicted entity.
 11. The apparatus of claim 10, wherein thetime-series neural network is trained by: obtaining a current trainingsample corresponding to a sample entity and an attribute tag of thesample entity regarding the preset transaction attribute; executing, foreach historical relational network of T historical relational networkssequentially arranged under the temporal order, a node vector fusionoperation of the neighbor nodes, to determine T description vectorsrespectively corresponding to the sample entity under the T relationalnetworks, wherein T is an integer, and the T historical relationalnetworks are established under T time points having the preset timeinterval; sequentially inputting, under the temporal order of thecorresponding historical relational networks, the T description vectorsinto a selected time-series neural network, and obtaining a sampleoutput result of the time-series neural network for the sample entity;and adjusting, with an objective of matching the sample output resultwith the attribute tag, model parameters to train the time-series neuralnetwork.
 12. The apparatus of claim 10, wherein the transactioncharacteristics in the node vector corresponding to a single node areextracted through descriptive information at a corresponding historicaltime point of a corresponding relational network, or extracted throughdescriptive information within a time interval between the correspondinghistorical time point and a previous historical time point of thecorresponding relational network.
 13. The apparatus of claim 10, whereinthe to-be-predicted entity is a user, the entities in each of thehistorical relational networks comprises a plurality of users and atleast one of: an IP identifier or a WIFI identifier for a user device toaccess a network, and an application installed on the user device. 14.The apparatus of claim 10, wherein the historical relational networkscomprise a first relational network, the to-be-predicted entitycorresponds to a first node in the first relational network, and thedetermining, for each of the historical relational networks and throughvector fusion of the neighbor nodes, the plurality of descriptionvectors of the to-be-predicted entity comprises: processing therelational network through a multilayer graph neural network, themultilayer graph neural network updates, after processing of a currentlayer is done, the node vector of the first node based on a weightedresult from a product of the node vector of each neighbor node updatedby the previous layer and an auxiliary weight matrix; and using the nodevector of the first node updated by the last layer of the graph neuralnetwork as a first description vector of the first node corresponding tothe first relational network.
 15. The apparatus of claim 14, wherein theneighbor nodes of the first node comprise a second node, and at thecurrent layer, a weight of the second node relative to the first node isdetermined by: an exponential form-based normalization result of asimilarity level between the node vector of the first node updated bythe previous layer and the node vector of the second node updated by theprevious layer, relative to a sum of similarity levels between the nodevector of the first node updated by the previous layer and the nodevector of each of the neighbor nodes updated by the previous layer. 16.The apparatus of claim 15, wherein the similarity level between the nodevector of the first node updated by the previous layer and the nodevector of the second node updated by the previous layer is determinedby: concatenating the node vector of the first node updated by theprevious layer and the node vector of the second node updated by theprevious layer to form a concatenation vector; performing, through aspatial auxiliary weight matrix, a dimensionality reduction process onthe concatenation vector to obtain an intermediate vector having presetdimensions; and processing, through an auxiliary vector having thepreset dimensions, the intermediate vector to obtain the similaritylevel between the node vector of the first node updated by the previouslayer and the node vector of the second node updated by the previouslayer.
 17. The apparatus of claim 10, wherein the output result is anoutput vector, the determining, according to the output result, theattribute state or the attribute category of the preset transactionattribute for the to-be-predicted entity comprises: processing theoutput vector through a fully connected neural network; and determining,through an obtained processing result, the attribute state or theattribute category of the preset transaction attribute for theto-be-predicted entity.
 18. The apparatus of claim 10, wherein theoutput result is an output vector, the attribute states or attributecategories of the preset transaction attribute for the to-be-predictedentity further respectively correspond to description vectors, and thedetermining, according to the output result, the attribute state or theattribute category of the preset transaction attribute for theto-be-predicted entity comprises: determining respective correspondingsimilarity levels between the output vector and each of the descriptionvectors; and determining an attribute state or attribute categorycorresponding to a description vector having the highest similaritylevel as the attribute state or attribute category of the presettransaction attribute for the to-be-predicted entity during a presettime period.
 19. A non-transitory computer-readable storage mediumhaving instructions stored thereon executable by a processor to causethe processor to perform operations comprising: obtaining a plurality ofhistorical relational networks sequentially arranged under a temporalorder, wherein the historical relational networks respectivelycorrespond to historical time points having a preset time interval, eachof the historical relational networks comprises a plurality of nodes,each node corresponds to a plurality of entities comprising ato-be-predicted entity, each of the historical relational networks isconfigured to describe association relationships between the pluralityof entities corresponding to a corresponding historical time point, andeach node corresponds to a node vector composed of transactioncharacteristics extracted based on descriptive information of thecorresponding entities; determining, for each of the historicalrelational networks and through vector fusion of neighbor nodes, aplurality of description vectors of the to-be-predicted entity;processing, through a pre-trained time-series neural network, thedescription vectors to obtain an output result; and determining,according to the output result, an attribute state or an attributecategory of the preset transaction attribute for the to-be-predictedentity.
 20. The non-transitory computer-readable storage medium of claim19, wherein the time-series neural network is trained by: obtaining acurrent training sample corresponding to a sample entity and anattribute tag of the sample entity regarding the preset transactionattribute; executing, for each historical relational network of Thistorical relational networks sequentially arranged under the temporalorder, a node vector fusion operation of the neighbor nodes, todetermine T description vectors respectively corresponding to the sampleentity under the T relational networks, wherein T is an integer, and theT historical relational networks are established under T time pointshaving the preset time interval; sequentially inputting, under thetemporal order of the corresponding historical relational networks, theT description vectors into a selected time-series neural network, andobtaining a sample output result of the time-series neural network forthe sample entity; and adjusting, with an objective of matching thesample output result with the attribute tag, model parameters to trainthe time-series neural network.